The present disclosure relates to using a pure convolutional neural network to generate a heat map of an image that identifies location regions of objects within the image.
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures. Various deep learning architectures are used in fields such as computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics.
Pattern recognition systems may employ classifiers that utilize deep learning architectures to generate decisions for a set of input data. The classifiers may utilize algorithms that are structured in the form of a deep learning architecture, such as a convolutional neural network, which includes various combinations of layers to arrive at a decision. Depending on the application context, the input data may be a piece of text, an image, a sound sequence, a set of numbers, or any other representation of a piece of information, an object, an action, or an event.
Traditional convolutional neural networks typically have a set of fully connected layers that combine information from across an image to detect an object in an image. Although these traditional convolutional neural networks are useful in detecting a general object location, the traditional convolutional neural networks sacrifice resolution accuracy and the ability to identify multiple objects within an image due to their convolutional and combinatory nature.